The present invention relates to a control system for controlling non-linear processes having more than one region of process operation. In particular, the present invention relates to a fuzzy logic controller which utilizes an auxiliary process variable for determining a region of process operation and selecting a fuzzy membership function for application to a fuzzy input variable to provide a process control input that corresponds to the region of process operation.
Fuzzy logic involves a series of fuzzy control rules which are expressed by a fuzzy implication of the form "if . . . then . . . ." These fuzzy implications include fuzzy variables which are often referred to as "linguistic variables". Fuzzy reasoning or inferences are accomplished by the application of the fuzzy variable to the fuzzy rules.
Fuzzy logic has been used in process control applications as described for example in Tanaka et al., U.S. Pat. No. 5,158,024 incorporated herein by reference. In these control applications, the fuzzy logic forms a fuzzy controller for controlling process parameters. A typical fuzzy logic controller is composed of three basic parts: input signal fuzzification, a fuzzy engine that handles rule inference, and defuzzification that generates continuous signals for actuators such as control valves.
There are several advantages to the use of fuzzy control of process parameters. One advantage is that human experience can easily be integrated into the fuzzy controller because the fuzzy control rules and the fuzzy variable are well suited to the human thought process.
Another advantage of the use of a fuzzy controller is the non-linearity resulting from fuzzification, application of the fuzzy rules to the fuzzy variables, and defuzzification. This non-linearity inherent in the fuzzy control process makes fuzzy controllers well suited to non-linear process control.
However, process controllers that are presently used for process control tend to make use of a process error signal and a change in process error signal as controller inputs for determining a process control or process input signal. These controller inputs do not provide sufficient information to the fuzzy controller so that the process control signal can account for any process non-linearities. Therefore, these fuzzy logic controllers are not capable of compensating for process non-linearities.
There is a present need for fuzzy controllers capable of providing a process control signal for compensating a process having a non-linear process variable such as process gain. This fuzzy controller should be capable of compensating the process throughout the different regions of non-linearity so that the control performance is uniform.